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London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure

Authors Hall JA, Barrett G, Copas A, Stephenson J

Received 15 September 2016

Accepted for publication 9 December 2016

Published 6 April 2017 Volume 2017:8 Pages 43—56

DOI https://doi.org/10.2147/PROM.S122420

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Akshita Wason

Peer reviewer comments 3

Editor who approved publication: Professor Liana Castel

Jennifer A Hall,1 Geraldine Barrett,1 Andrew Copas,2 Judith Stephenson1

1Research Department of Reproductive Health, UCL Institute for Women’s Health, 2Department of Infection & Population Health, UCL Institute of Epidemiology and Health Care, London, UK

Background: The London Measure of Unplanned Pregnancy (LMUP) is a psychometrically validated measure of the degree of intention of a current or recent pregnancy. The LMUP is increasingly being used worldwide, and can be used to evaluate family planning or ­preconception care programs. However, beyond recommending the use of the full LMUP scale, there is no published guidance on how to use the LMUP as an outcome measure. Ordinal logistic regression has been recommended informally, but studies published to date have all used binary logistic regression and dichotomized the scale at different cut points. There is thus a need for evidence-based guidance to provide a standardized methodology for multivariate analysis and to enable comparison of results. This paper makes recommendations for the regression method for analysis of the LMUP as an outcome measure.
Materials and methods: Data collected from 4,244 pregnant women in Malawi were used to compare five regression methods: linear, logistic with two cut points, and ordinal logistic with either the full or grouped LMUP score. The recommendations were then tested on the original UK LMUP data.
Results: There were small but no important differences in the findings across the regression models. Logistic regression resulted in the largest loss of information, and assumptions were violated for the linear and ordinal logistic regression. Consequently, robust standard errors were used for linear regression and a partial proportional odds ordinal logistic regression model attempted. The latter could only be fitted for grouped LMUP score.
Conclusion: We recommend the linear regression model with robust standard errors to make full use of the LMUP score when analyzed as an outcome measure. Ordinal logistic regression could be considered, but a partial proportional odds model with grouped LMUP score may be required. Logistic regression is the least-favored option, due to the loss of information. For logistic regression, the cut point for un/planned pregnancy should be between nine and ten. These recommendations will standardize the analysis of LMUP data and enhance comparability of results across studies.

Keywords: ordinal outcomes, multivariate regression, London Measure of Unplanned Pregnancy, pregnancy intention, pregnancy planning, epidemiology

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